import torch
from torch import nn
from torch.nn import functional as F
from attention import SelfAttention

class VAE_ResidualBlock(nn.Module):
    def __init__(self, input_channels, output_channels):
        super().__init__()
        self.groupnorm_1 = nn.GroupNorm(32, input_channels)
        self.conv_1 = nn.Conv2d(input_channels, output_channels, kernel_size=3, padding=1)

        self.groupnorm_2 = nn.GroupNorm(32, output_channels)
        self.conv_2 = nn.Conv2d(output_channels, output_channels, kernel_size=3, padding=1)

        if input_channels == output_channels:
            self.residual_layer = nn.Identity()
        else:
            self.residual_layer = nn.Conv2d(input_channels, output_channels, kernel_size=1, padding=0)
    
    def forward(self, x):
        residue = x 
        x = self.groupnorm_1(x)
        x = F.silu(x)
        x = self.conv_1(x)
        x = self.groupnorm_2(x)
        x = F.silu(x)
        x = self.conv_2(x)
        return x + self.residual_layer(residue)

class VAE_AttentionBlock(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.groupnorm = nn.GroupNorm(32, channels)
        self.attention = SelfAttention(1, channels) 
    
    def forward(self, x):
        residue = x
        x = self.groupnorm(x)
        n, c, h, w = x.shape
        #(batch_size, features, height, width) -> (batch_size, features, height * width)
        x = x.view((n, c, h * w))
        x = x.transpose(-1, -2)
        x = self.attention(x)
        x = x.transpose(-1, -2)
        x = x.view((n, c, h, w))
        return x + residue

class VAE_Decoder(nn.Sequential):
    def __init__(self):
        super().__init__(
            nn.Conv2d(4, 4, kernel_size=1, padding=0),
            nn.Conv2d(4, 512, kernel_size=3, padding=1),
            VAE_ResidualBlock(512, 512),
            VAE_AttentionBlock(512),
            VAE_ResidualBlock(512, 512),
            VAE_ResidualBlock(512, 512),
            VAE_ResidualBlock(512, 512),
            VAE_ResidualBlock(512, 512),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            VAE_ResidualBlock(512, 512),
            VAE_ResidualBlock(512, 512),
            VAE_ResidualBlock(512, 512),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            VAE_ResidualBlock(512, 256),
            VAE_ResidualBlock(256, 256),
            VAE_ResidualBlock(256, 256),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            VAE_ResidualBlock(256, 128),
            VAE_ResidualBlock(128, 128),
            VAE_ResidualBlock(128, 128),
            nn.GroupNorm(32,128),
            nn.SiLU(),
            nn.Conv2d(128, 3, kernel_size=3, padding=1)
        )
    def forward(self, x):
        x /= 0.18215
        for module in self:
            x = module(x)
        return x
    









